Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory390.8 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Air Quality is highly overall correlated with CO and 4 other fieldsHigh correlation
CO is highly overall correlated with Air Quality and 7 other fieldsHigh correlation
Humidity is highly overall correlated with COHigh correlation
NO2 is highly overall correlated with Air Quality and 4 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with PM10High correlation
Population_Density is highly overall correlated with CO and 1 other fieldsHigh correlation
Proximity_to_Industrial_Areas is highly overall correlated with Air Quality and 6 other fieldsHigh correlation
SO2 is highly overall correlated with Air Quality and 4 other fieldsHigh correlation
Temperature is highly overall correlated with Air Quality and 4 other fieldsHigh correlation

Reproduction

Analysis started2025-01-02 08:17:31.312099
Analysis finished2025-01-02 08:18:01.471814
Duration30.16 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

High correlation 

Distinct362
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.02902
Minimum13.4
Maximum58.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:01.587960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum13.4
5-th percentile21
Q125.1
median29
Q334
95-th percentile42.6
Maximum58.6
Range45.2
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation6.7206614
Coefficient of variation (CV)0.22380555
Kurtosis0.51316206
Mean30.02902
Median Absolute Deviation (MAD)4.4
Skewness0.75218681
Sum150145.1
Variance45.167289
MonotonicityNot monotonic
2025-01-02T17:18:01.769286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.8 45
 
0.9%
26.7 43
 
0.9%
29.4 42
 
0.8%
26.3 41
 
0.8%
27.4 41
 
0.8%
23.6 41
 
0.8%
24.6 38
 
0.8%
32.2 38
 
0.8%
27.8 37
 
0.7%
26.2 36
 
0.7%
Other values (352) 4598
92.0%
ValueCountFrequency (%)
13.4 1
< 0.1%
14.1 1
< 0.1%
14.4 1
< 0.1%
15.3 1
< 0.1%
15.4 1
< 0.1%
15.5 1
< 0.1%
16 1
< 0.1%
16.1 1
< 0.1%
16.4 2
< 0.1%
16.5 1
< 0.1%
ValueCountFrequency (%)
58.6 1
< 0.1%
57.8 1
< 0.1%
57.7 1
< 0.1%
57.2 1
< 0.1%
56.5 1
< 0.1%
56 2
< 0.1%
55.9 1
< 0.1%
55.7 1
< 0.1%
55 1
< 0.1%
54.7 1
< 0.1%

Humidity
Real number (ℝ)

High correlation 

Distinct723
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.05612
Minimum36
Maximum128.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:01.949121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile45.1
Q158.3
median69.8
Q380.3
95-th percentile97.905
Maximum128.1
Range92.1
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.863577
Coefficient of variation (CV)0.22644098
Kurtosis-0.29031593
Mean70.05612
Median Absolute Deviation (MAD)10.9
Skewness0.28052793
Sum350280.6
Variance251.65307
MonotonicityNot monotonic
2025-01-02T17:18:02.129757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 20
 
0.4%
72.5 18
 
0.4%
67.6 18
 
0.4%
64.4 18
 
0.4%
64.6 18
 
0.4%
60.1 18
 
0.4%
67.9 17
 
0.3%
72.7 17
 
0.3%
65.8 17
 
0.3%
75.6 17
 
0.3%
Other values (713) 4822
96.4%
ValueCountFrequency (%)
36 1
 
< 0.1%
36.1 1
 
< 0.1%
36.3 1
 
< 0.1%
36.9 1
 
< 0.1%
38.2 2
< 0.1%
38.3 3
0.1%
38.4 1
 
< 0.1%
38.5 1
 
< 0.1%
38.6 2
< 0.1%
38.7 1
 
< 0.1%
ValueCountFrequency (%)
128.1 1
< 0.1%
124.7 1
< 0.1%
123 1
< 0.1%
122.3 1
< 0.1%
120.7 1
< 0.1%
120.5 2
< 0.1%
119.4 1
< 0.1%
117.3 1
< 0.1%
116.9 2
< 0.1%
116.3 1
< 0.1%

PM2.5
Real number (ℝ)

High correlation 

Distinct815
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.14214
Minimum0
Maximum295
Zeros20
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:02.304647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q14.6
median12
Q326.1
95-th percentile68.4
Maximum295
Range295
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation24.554546
Coefficient of variation (CV)1.2190634
Kurtosis13.033781
Mean20.14214
Median Absolute Deviation (MAD)8.8
Skewness2.89091
Sum100710.7
Variance602.92572
MonotonicityNot monotonic
2025-01-02T17:18:02.492815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 38
 
0.8%
1.1 37
 
0.7%
2 36
 
0.7%
0.7 35
 
0.7%
0.4 33
 
0.7%
2.3 32
 
0.6%
0.3 32
 
0.6%
2.8 32
 
0.6%
1 31
 
0.6%
2.5 31
 
0.6%
Other values (805) 4663
93.3%
ValueCountFrequency (%)
0 20
0.4%
0.1 30
0.6%
0.2 26
0.5%
0.3 32
0.6%
0.4 33
0.7%
0.5 24
0.5%
0.6 27
0.5%
0.7 35
0.7%
0.8 29
0.6%
0.9 25
0.5%
ValueCountFrequency (%)
295 1
< 0.1%
240.1 1
< 0.1%
216.9 1
< 0.1%
204 1
< 0.1%
193.1 1
< 0.1%
186.7 1
< 0.1%
173.9 1
< 0.1%
173.2 1
< 0.1%
169.2 1
< 0.1%
168.6 2
< 0.1%

PM10
Real number (ℝ)

High correlation 

Distinct955
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.21836
Minimum-0.2
Maximum315.8
Zeros1
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size39.2 KiB
2025-01-02T17:18:02.684066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-0.2
5-th percentile5.8
Q112.3
median21.7
Q338.1
95-th percentile84.705
Maximum315.8
Range316
Interquartile range (IQR)25.8

Descriptive statistics

Standard deviation27.349199
Coefficient of variation (CV)0.9050524
Kurtosis10.273039
Mean30.21836
Median Absolute Deviation (MAD)11.2
Skewness2.5348148
Sum151091.8
Variance747.97871
MonotonicityNot monotonic
2025-01-02T17:18:02.869865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1 28
 
0.6%
16.3 26
 
0.5%
10.9 25
 
0.5%
14.1 24
 
0.5%
18.9 24
 
0.5%
8.4 24
 
0.5%
8.8 23
 
0.5%
8 22
 
0.4%
15.5 22
 
0.4%
14.9 22
 
0.4%
Other values (945) 4760
95.2%
ValueCountFrequency (%)
-0.2 1
 
< 0.1%
0 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
1 1
 
< 0.1%
1.3 1
 
< 0.1%
1.5 2
< 0.1%
1.7 1
 
< 0.1%
1.9 4
0.1%
2 2
< 0.1%
ValueCountFrequency (%)
315.8 1
< 0.1%
261.5 1
< 0.1%
240 1
< 0.1%
221.6 1
< 0.1%
212.6 1
< 0.1%
209.8 1
< 0.1%
194.7 1
< 0.1%
190.7 1
< 0.1%
190 1
< 0.1%
188.2 1
< 0.1%

NO2
Real number (ℝ)

High correlation 

Distinct445
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.4121
Minimum7.4
Maximum64.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:03.048212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile13.595
Q120.1
median25.3
Q331.9
95-th percentile43.1
Maximum64.9
Range57.5
Interquartile range (IQR)11.8

Descriptive statistics

Standard deviation8.8953564
Coefficient of variation (CV)0.33679095
Kurtosis0.24846135
Mean26.4121
Median Absolute Deviation (MAD)5.8
Skewness0.63878267
Sum132060.5
Variance79.127365
MonotonicityNot monotonic
2025-01-02T17:18:03.226974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.2 38
 
0.8%
25.3 34
 
0.7%
26.6 33
 
0.7%
23.1 31
 
0.6%
23 31
 
0.6%
23.5 31
 
0.6%
25.4 30
 
0.6%
20.9 30
 
0.6%
23.4 29
 
0.6%
22.9 29
 
0.6%
Other values (435) 4684
93.7%
ValueCountFrequency (%)
7.4 1
 
< 0.1%
8.5 1
 
< 0.1%
9.1 1
 
< 0.1%
9.2 1
 
< 0.1%
9.3 1
 
< 0.1%
9.9 2
< 0.1%
10 3
0.1%
10.1 2
< 0.1%
10.2 2
< 0.1%
10.3 2
< 0.1%
ValueCountFrequency (%)
64.9 1
< 0.1%
62.1 1
< 0.1%
59.3 2
< 0.1%
57.3 1
< 0.1%
56.8 1
< 0.1%
56.6 1
< 0.1%
56.4 1
< 0.1%
56.1 1
< 0.1%
56 1
< 0.1%
55.8 1
< 0.1%

SO2
Real number (ℝ)

High correlation 

Distinct348
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.01482
Minimum-6.2
Maximum44.9
Zeros5
Zeros (%)0.1%
Negative30
Negative (%)0.6%
Memory size39.2 KiB
2025-01-02T17:18:03.407687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-6.2
5-th percentile2.3
Q15.1
median8
Q313.725
95-th percentile23.6
Maximum44.9
Range51.1
Interquartile range (IQR)8.625

Descriptive statistics

Standard deviation6.7503034
Coefficient of variation (CV)0.67403142
Kurtosis1.3292471
Mean10.01482
Median Absolute Deviation (MAD)3.7
Skewness1.1667723
Sum50074.1
Variance45.566596
MonotonicityNot monotonic
2025-01-02T17:18:03.586715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.7 61
 
1.2%
5.9 57
 
1.1%
4.5 55
 
1.1%
4.9 54
 
1.1%
5.3 53
 
1.1%
6.3 53
 
1.1%
5 52
 
1.0%
6.4 49
 
1.0%
4.6 48
 
1.0%
5.1 48
 
1.0%
Other values (338) 4470
89.4%
ValueCountFrequency (%)
-6.2 1
< 0.1%
-4.1 1
< 0.1%
-3.4 1
< 0.1%
-2.8 1
< 0.1%
-1.9 1
< 0.1%
-1.7 1
< 0.1%
-1.4 1
< 0.1%
-1.2 1
< 0.1%
-0.6 2
< 0.1%
-0.5 2
< 0.1%
ValueCountFrequency (%)
44.9 1
< 0.1%
42.3 1
< 0.1%
40.7 1
< 0.1%
40.5 1
< 0.1%
39.6 1
< 0.1%
38.7 1
< 0.1%
37.6 2
< 0.1%
36.8 2
< 0.1%
36.5 1
< 0.1%
36.2 1
< 0.1%

CO
Real number (ℝ)

High correlation 

Distinct265
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.500354
Minimum0.65
Maximum3.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:03.758261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile0.88
Q11.03
median1.41
Q31.84
95-th percentile2.53
Maximum3.72
Range3.07
Interquartile range (IQR)0.81

Descriptive statistics

Standard deviation0.54602667
Coefficient of variation (CV)0.36393189
Kurtosis0.20965338
Mean1.500354
Median Absolute Deviation (MAD)0.39
Skewness0.8790677
Sum7501.77
Variance0.29814512
MonotonicityNot monotonic
2025-01-02T17:18:03.937072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98 88
 
1.8%
0.99 88
 
1.8%
1.02 87
 
1.7%
1.03 85
 
1.7%
1.01 84
 
1.7%
1.04 80
 
1.6%
0.94 73
 
1.5%
0.97 73
 
1.5%
0.92 70
 
1.4%
1 70
 
1.4%
Other values (255) 4202
84.0%
ValueCountFrequency (%)
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.69 1
 
< 0.1%
0.72 4
 
0.1%
0.73 4
 
0.1%
0.74 4
 
0.1%
0.75 2
 
< 0.1%
0.76 9
0.2%
0.77 9
0.2%
0.78 10
0.2%
ValueCountFrequency (%)
3.72 1
 
< 0.1%
3.67 1
 
< 0.1%
3.65 1
 
< 0.1%
3.61 1
 
< 0.1%
3.54 1
 
< 0.1%
3.48 2
< 0.1%
3.4 1
 
< 0.1%
3.37 2
< 0.1%
3.36 3
0.1%
3.35 2
< 0.1%

Proximity_to_Industrial_Areas
Real number (ℝ)

High correlation 

Distinct179
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4254
Minimum2.5
Maximum25.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:04.116903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.5
Q15.4
median7.9
Q311.1
95-th percentile14.4
Maximum25.8
Range23.3
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.6109437
Coefficient of variation (CV)0.42857831
Kurtosis-0.23374824
Mean8.4254
Median Absolute Deviation (MAD)2.8
Skewness0.46975156
Sum42127
Variance13.038915
MonotonicityNot monotonic
2025-01-02T17:18:04.295100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1 112
 
2.2%
10.2 112
 
2.2%
10.3 107
 
2.1%
10.1 105
 
2.1%
5.2 99
 
2.0%
5.4 97
 
1.9%
10.4 96
 
1.9%
5.6 90
 
1.8%
10.5 88
 
1.8%
11.1 86
 
1.7%
Other values (169) 4008
80.2%
ValueCountFrequency (%)
2.5 14
 
0.3%
2.6 28
0.6%
2.7 24
0.5%
2.8 26
0.5%
2.9 16
 
0.3%
3 21
 
0.4%
3.1 12
 
0.2%
3.2 19
 
0.4%
3.3 23
 
0.5%
3.4 58
1.2%
ValueCountFrequency (%)
25.8 1
 
< 0.1%
25.2 1
 
< 0.1%
24.8 1
 
< 0.1%
23.4 1
 
< 0.1%
21.8 1
 
< 0.1%
21.7 1
 
< 0.1%
21.6 1
 
< 0.1%
21.5 1
 
< 0.1%
20.8 1
 
< 0.1%
20 3
0.1%

Population_Density
Real number (ℝ)

High correlation 

Distinct683
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497.4238
Minimum188
Maximum957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-01-02T17:18:04.469701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile249
Q1381
median494
Q3600
95-th percentile765
Maximum957
Range769
Interquartile range (IQR)219

Descriptive statistics

Standard deviation152.75408
Coefficient of variation (CV)0.30709042
Kurtosis-0.47380952
Mean497.4238
Median Absolute Deviation (MAD)109.5
Skewness0.20423108
Sum2487119
Variance23333.81
MonotonicityNot monotonic
2025-01-02T17:18:04.658235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
494 24
 
0.5%
454 23
 
0.5%
543 22
 
0.4%
471 20
 
0.4%
501 20
 
0.4%
538 20
 
0.4%
511 19
 
0.4%
438 19
 
0.4%
506 19
 
0.4%
485 19
 
0.4%
Other values (673) 4795
95.9%
ValueCountFrequency (%)
188 1
 
< 0.1%
189 3
0.1%
191 1
 
< 0.1%
193 2
< 0.1%
194 2
< 0.1%
196 3
0.1%
197 1
 
< 0.1%
198 2
< 0.1%
199 3
0.1%
200 4
0.1%
ValueCountFrequency (%)
957 1
< 0.1%
951 1
< 0.1%
939 1
< 0.1%
937 1
< 0.1%
934 2
< 0.1%
933 1
< 0.1%
927 2
< 0.1%
924 1
< 0.1%
923 1
< 0.1%
914 1
< 0.1%

Air Quality
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Good
2000 
Moderate
1500 
Poor
1000 
Hazardous
500 

Length

Max length9
Median length4
Mean length5.7
Min length4

Characters and Unicode

Total characters28500
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good 2000
40.0%
Moderate 1500
30.0%
Poor 1000
20.0%
Hazardous 500
 
10.0%

Length

2025-01-02T17:18:04.842689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-02T17:18:04.979305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
good 2000
40.0%
moderate 1500
30.0%
poor 1000
20.0%
hazardous 500
 
10.0%

Most occurring characters

ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Interactions

2025-01-02T17:17:59.187860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-02T17:17:51.481252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:53.623340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:54.744665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.845088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.927992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.072112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.310811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:33.429473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:44.455323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:51.910288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-02T17:17:54.857176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.961338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.048261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.194463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.435233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:35.045442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:48.024470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:52.677320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:53.862109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:54.972806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.073039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.168516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.311221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.567332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:38.689841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:48.143024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:52.910298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:53.987828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.098320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.189903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.304189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.437455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.703819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:38.828141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:48.370724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:52.998117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:54.117980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.226947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.311639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.437463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.563615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.831441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:39.103393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:48.659204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:53.119020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:54.241489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.347324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.425866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.561473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.686457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.956869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:39.387043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:49.586885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:53.236606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:54.355777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-02T17:17:54.489322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.589580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.665864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.807724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:58.936543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:18:00.972985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:41.681915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:50.832724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:53.487753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:54.611888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:55.710366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:56.788009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:57.927672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T17:17:59.051680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-02T17:18:05.086397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Air QualityCOHumidityNO2PM10PM2.5Population_DensityProximity_to_Industrial_AreasSO2Temperature
Air Quality1.0000.7510.4110.5410.3210.2400.4370.6360.5280.520
CO0.7511.0000.5510.7090.5850.3790.573-0.7720.6880.689
Humidity0.4110.5511.0000.4750.3850.2550.388-0.4870.4430.452
NO20.5410.7090.4751.0000.4850.3140.486-0.6450.5730.583
PM100.3210.5850.3850.4851.0000.9150.389-0.5300.4730.476
PM2.50.2400.3790.2550.3140.9151.0000.250-0.3370.3060.306
Population_Density0.4370.5730.3880.4860.3890.2501.000-0.5060.4550.460
Proximity_to_Industrial_Areas0.636-0.772-0.487-0.645-0.530-0.337-0.5061.000-0.627-0.628
SO20.5280.6880.4430.5730.4730.3060.455-0.6271.0000.568
Temperature0.5200.6890.4520.5830.4760.3060.460-0.6280.5681.000

Missing values

2025-01-02T17:18:01.150892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-02T17:18:01.373191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureHumidityPM2.5PM10NO2SO2COProximity_to_Industrial_AreasPopulation_DensityAir Quality
029.859.15.217.918.99.21.726.3319Moderate
128.375.62.312.230.89.71.646.0611Moderate
223.174.726.733.824.412.61.635.2619Moderate
327.139.16.16.313.55.31.1511.1551Good
426.570.76.916.021.95.61.0112.7303Good
539.496.614.635.542.917.91.823.1674Hazardous
641.782.51.715.831.112.71.804.6735Poor
731.059.65.016.824.213.61.386.3443Moderate
829.493.810.322.745.111.82.035.4486Poor
933.280.511.124.432.015.31.694.9535Poor
TemperatureHumidityPM2.5PM10NO2SO2COProximity_to_Industrial_AreasPopulation_DensityAir Quality
499046.893.811.825.433.828.73.273.7589Hazardous
499131.880.222.434.129.74.91.229.4580Moderate
499229.856.76.814.023.04.51.1011.4567Good
499334.977.732.347.117.411.51.638.8541Moderate
499431.161.027.131.113.03.80.9813.4278Good
499540.674.1116.0126.745.525.72.112.8765Hazardous
499628.196.96.925.025.310.81.545.7709Moderate
499725.978.214.222.134.87.81.639.6379Moderate
499825.344.421.429.023.75.70.8911.6241Good
499924.177.981.794.323.210.51.388.3461Moderate